Maize Yield Research Articles

High-efficiency fungicide screening and field control efficacy of maize southern corn rust

Southern corn rust caused by Puccinia dothidea is becoming one of the main diseases of maize planting in China, which seriously threatens the yield of maize in China. Currently, there is no registered pesticide to control southern corn rust in China. Therefore, it is urgent to screen out efficient pesticides to control southern corn rust and reduce the harm of multistem rust to maize yield in China. In the present study, the indoor virulence determination of spore-germination method and 2 years of field efficacy tests were used to screen the fungicides with good prevention and control efficacy on maize southern rust. The results of the indoor toxicity test in 2023 showed that the EC50 of P. dothidea to benzoflumizole was 0.0085 mg/L, and the sensitivity to thifluzamide was relatively poor, with an EC50 of 1.2585 mg/L. The mixture of pyraclostrobin and benzoflumizole at a ratio of 4:1 had the optimal synergistic effect, with an EC50 of 0.0181 mg/L and a cotoxicity coefficient of 128.36. The results of the field efficacy test revealed that 60 g/ha benzovinfluconazole had a good field control effect, and the control effect was 83.09%–85.95%. The combination of 30 g/ha benzodifluconazole and 93.75 g/ha pyraclostrobin had the optimal control effect, and the control effect was 86.92%–88.58% and safe for corn. The results indicated that the application of benzovindiflupyr and its mixture with pyraclostrobin had a good effect on the prevention and control of southern corn rust, which could provide a theoretical basis for the prevention and control of southern corn rust in production and scientific medication.

Deep return of straw improves topsoil health and maize productivity more efficiently than shallow return

AbstractStraw return is an important nature‐based solution to identify optimal management practices to maintain cropland soil health and food security. However, there is still limited information available regarding the impacts of different straw return strategies on soil health, especially when considering a comprehensive assessment of soil physical, chemical and biological characteristics. Soil microorganisms are the core and key to maintaining soil health. The present research aims to determine the impact of straw return to different depths on soil health index (SHI) and crop productivity across different soil layers. We conducted a 3–year field experiment with four treatments: a blank control with no straw return (NR), straw return treatments with shallow return (SR), mulch return (MR) and deep return (DR), and the straw application rate was 10,500 kg/ha. The results showed that it was the microbial indicators that dominated the effects on soil health, rather than soil organic carbon (SOC). Specifically, we found that the SR treatment exhibited the highest extracellular enzyme activities of both hydrolases and oxidases in the subsoil, and the peroxidase (PEO) activity was 758.99 nmol/g/h. In addition, compared with MR and DR treatments, the abundance of symbiotroph fungi in SR treatment increased by 78.41% and 54.38%, releasing more nutrients to the soil. Hence, SR treatment increased SHI mainly by increasing the PEO activity and NO3−–N content, and SR treatment significantly increased SHI by 25.59% and 22.16% compared with MR and DR treatments. By contrast, the DR treatment greatly increased the topsoil SHI by 43.33% and 58.98% compared with SR and SM treatments, mainly due to the enhanced microbial biomass N (MBN) and dissolved organic nitrogen (DON). The DR treatment in topsoil had the lowest soil bulk density (BD) which enhanced Bacteroidota but decreased Methylomirabilota abundance, thus resulting in the accumulation of MBN. Moreover, DR treatment led the highest maize yield which increased with an increasing SHI. To conclude, DR treatment is an effective method to improve SHI and crop productivity in nutrient‐rich topsoil, while SR treatment has a much better effect in the subsoil. Our results will provide a theoretical paradigm for selecting straw return strategies, which is crucial for promoting soil health and food productivity, achieving high crop yields, and supporting sustainable agricultural development.

Maize crop residue cover mapping using Sentinel-2 MSI data and random forest algorithms

The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and, therefore, the areal extent of crop residue cover (CRC) could provide a rapid measure of the sustainability of agricultural production systems in a region. Recognizing the limitations of traditional CRC methods, a new method is proposed for estimating the spatial and temporal distribution of maize residue cover (MRC) in the Jilin Province, NE China. The method used random forest (RF) algorithms, 13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data. The tillage indices with the best predictive performance were STI and NDTI (R2 of 0.85 and 0.84, respectively). Among the texture features, the best-fitting was Band8AMean-5*5 (R2 of 0.56 and 0.54 for the line-transect and photographic methods, respectively). Based on MSE and InNodePurity, the optimal combination of RF algorithm for the line-transect method was STI, NDTI, NDI7, NDRI5, SRNDI, NDRI6, NDRI7 and Band3Mean-3*3. Likewise, the optimal combination of RF algorithm for the photographic method was STI, NDTI, NDI7, SRNDI, NDRI6, NDRI5, NDRI9 and Band3Mean-3*3. Regional distribution of MRC in the Jilin Province, estimated using the RF prediction model, was higher in the central and southeast sections than in the northwest. That distribution was in line with the spatial heterogeneity of maize yield in the region. These findings showed that the RF algorithm can be used to map regional MRC and, therefore, represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.

Hedging irrigated maize crop yields using temperature derivatives in Malawi

Agriculture production yield varies with weather changes. This causes farmers incur loses. For instance, extreme temperature leads to low maize yield. This study describes incomplete temperature weather derivatives in agriculture markets and applies risk management hedging techniques. It focuses on hedging crop yield against extreme temperatures during irrigation, farming which is done without greenhouse. This study's primary goal is to hedge irrigated maize crop yields using temperature derivatives. This is achieved firstly by modelling a daily average temperature stochastical model. Then, deriving statistical properties of the model based on 1990–2020 Kasungu District Temperature historical data. Lastly, pricing temperature derivatives to hedge maize crop yield. This study uses 1990–2020 Kasungu District Temperature historical data. Then a stochastically Ornstein-Uhlenbeck process with the time-varying speed of reversion, seasonal mean, and local volatility that depends on the local average temperature was proposed. Based on the average temperature model, down and output barrier option pricing models for average temperature and growing degree day (GDD) are applied. The study results shows that when the GDD is above the barrier level the barrier option does not knock out. Hence gives the holder the right to buy the underlying asset since it does not reach or fall below a predefined level over the option's lifetime. If the GDD does not exceed the barrier level, then the farmer will not have to pay the premium because the option is invalid. On the same note, the farmer will have to exercise his right by paying the premium of calculated premium when the GDD exceeds the barrier level. In return for this, the farmer gets paid off which happens to be the difference between GDD and barrier multiplied $1. In line with Malawi's 2063 Millennium Development Goals (MDGs), this study acts as an eye opener for the government to put a policy on whether derivatives should be practised in our country hence, increase cash holding by improving the situation of the farmer and country.

Hierarchical Bayesian semi-parametric models for measurement error correction in determining optimal fertilizer application levels

Measurement errors present a substantial challenge in accurately determining optimal fertilizer application levels, directly impacting agricultural efficiency and cost-effectiveness. This study examines the use of Hierarchical Bayesian Semi-Parametric (HBS) models to correct these errors, thereby improving precision in agricultural decision-making. By applying these models to a decade of data from Uasin Gishu County, Kenya, we evaluated key variables including maize yield, land size, and fertilizer levels. The results indicate that the HBS models effectively mitigate both systematic and random errors, leading to more accurate fertilizer recommendations. This advancement supports better resource management and higher crop yields. Our findings underscore the value of Bayesian methods in agricultural data analysis and highlight the critical role of accurate measurement and correction in achieving optimal outcomes. The implications of this research extend to improved decision-making processes and more sustainable agricultural practices.

Climatic Variations Influenced Distribution and Productivity of Different Agroforestry Systems in Rajasthan, India

Farmers of Rajasthan conserve and promote scattered trees and shrubs growing on farmlands to dealwith climatic adversities. These woody perennials sustain livelihoods by providing economic, social, and environmental benefits. Tree-based farming systems were surveyed in all 10 agroclimatic zones (ACZs) of Rajasthan for screening best models and suggests for further improvement. Trees were assessed for density, and growth parameters in 0.5 ha plots, whereas agricultural crops yields were estimated in1 m2 plots laid at 1 m from tree trunk, canopy edge and 5 m away from trees canopy (control). There were 107 tree-crop combinations involving 14 trees and 21 crop species. P. cineraria-based systems were dominant in the arid western, whereas A. nilotica dominated in semi-arid to dry sub-humidregions. Average tree density was 11.7 trees ha-1 ranging from 3.3 tree ha-1 in ACZ-IIIb to 23.6 tree ha-1 in ACZ-IIa. Senegalia senegal showed highest (27.6 tree ha−1) density. Height and canopy diameter were 11.73 m and 10.35 m respectively, in irrigated ACZ- IIIb and decreased to 6.53 m and 5.02 m in ACZ-IIb, respectively. Both height and density decreased with the decrease in rainfall from south-east to north-west. D. sissoo and T. grandis were taller, S. oleoides was thicker and Z. mauritiana was shorter and thinner than other species. Grain yields varied from 0.66 in ACZ-Ia to 1.85 Mg ha-1 in ACZ-IIIa, but exhibited low yield as compared to control. Crop yield reduced by 22.9-47.3% between ACZs, 20.3-56.5% between agroforestry systems, and 15.9-79.8% between intercrops highlighting the interactive effects of these components. S. senegal, T. undulata, P. cineraria, Z. mauritiana, S. oleoides, V. tortilis and P. juliflora based systems showed 29.1-54.9% low, whereas V. nilotica, D. sissoo, A. indica, T. grandis and V. leucophloea based systems showed 4.5-51.7% high yield over the state average. The yield of groundnut (IIIa), cotton (IVa), barley (IIIa), jowar (IVa), maize (IVa), paddy (V), pearl millet (IIIa) and wheat (V) with A. nilotica was above state average

Effect of Sowing Methods, Weed Management and Growth Promoters on Yield and Quality Behavior of Kharif Maize (Zea mays L)

The present investigation was carried out during two consecutive kharif season in years 2022 and 2023 at students. Instructional Farm, Department of Agronomy, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur (U. P). The experiment was laid out in Split Plot Design keeping sowing methods in main plot and weed management practices in sub plots with growth promoters in sub – sub plots with three replications. There was two sowing methods viz; Conventional methods (S1) and Ridge methods (S2). Whereas weed management practices were five, viz; Weed Free (W1), Weedy Check (W2), Atrazine Pre-emergence @ 1.25 Kg /ha (W3), Halosulfuron methyl Post-emergence @ 65g a.i./ha (W4), Atrazine Pre-emergence @ 0.75 l/ha + Halosulfuron methyl Post-emergence @ 35g ai./ha (W5). and there was Growth promoters’ practices were three viz; Gibberellic acid (Sayish) (G1), Amino acid + Humic acid (Spring ever) (G2), Cytokinins + Enzymes (Ambition) (G3). The experiment was shown on 10th July during 2022 and 13rd July during 2023 using hybrid maize DKC-9144. The crop was harvested at full ripe stage on 11 October and 14 October, in 2022 and 2023, respectively. The highest grain yield, biological yield, stower yield, harvesting index, protein content and protein yield was found under the treatment of S2 (Ridge method) in sowing method, treatment W2 (weed free) in weed control methods and treatment G3 (Cytokinins + Enzymes) in plant growth promotor.

Liquid bio-slurry enhances the productivity of N-fertilized maize under field conditions in Ethiopia

Enhancing maize production and productivity is critical for ensuring small-scale farmers' food security. Yet, declining soil fertility poses a substantial challenge to cereal production, including maize, in developing countries. Farmers are currently finding liquid bio-slurry to be a valuable organic amendment because it improves maize productivity and food security by altering the physicochemical properties of the soil. However, extensive research on liquid bio-slurry as an organic amendment in Ethiopia is still limited. Hence, a field experiment was conducted at two sites to identify the optimal combination of inorganic nitrogen (N) and liquid bio-slurry to improve maize productivity. The trial involved the application of two mineral N fertilizers and five different rates of liquid bio-slurry in a randomized complete block design with three replications. The results revealed that the main effects of mineral N and liquid bio-slurry were significant (p< 0.05) on most of the parameters examined. The interaction between mineral N x liquid bio-slurry had a significant (p< 0.05) effect on plant height, ear length, number of rows ear-1, number of kernels ear-1, above-ground dry biomass yield, hundred-grain weight, grain yield, and straw yield. The highest grain yield (8,220 kg ha-1) was achieved by combining 46 kg N ha-1 with 18 t liquid bio-slurry ha-1. Therefore, 46 kg N ha-1 with 18 t liquid bio-slurry ha-1 is highly recommended for increasing maize yield in the study sites and other areas with similar agroecological zones.

Quantification of spatial-temporal light interception of crops in different configurations of soybean-maize strip intercropping.

Intercropping can improve light interception and crop yield on limited farmlands. The light interception rate in intercropping is determined by row configuration. Quantifying the spatio-temporal light interception of intercrops is very important for improving crop yields by optimizing the row configuration. A two-year field experiment was conducted at two sites to quantify the responses of the light interception rate of intercrops to five treatments: two rows of maize alternated with three rows of soybean (2M3S), two rows of maize alternated four rows of soybean (2M4S), two rows of maize alternated five rows of soybean (2M5S), sole soybean (SS), and sole maize (SM). We developed a multiple regression model based on the sine of the solar elevation angle (sin(h)) and crop leaf area density (LAD) to quantify the spatio-temporal light interception of intercrops. The predicted light interception rate was positively correlated with the measured values of photosynthetically active radiation (R2 > 0.814) and dry matter (R2 > 0.830). Increasing soybean rows led to an increase in light interception of both soybean and the lower layer of maize. However, this also resulted in a decrease in light interception in the upper layer of maize. At the two sites, compared to 2M3S, the annual average cumulative light interception of soybean in 2M5S increased by 44.73% and 47.18%, that of the lower layer of maize in 2M5S increased by 9.25% and 8.04%, and that of whole canopy of maize decreased by 13.77% and 17.74% respectively. The changes in dry matter and yield of intercrops were consistent with the change in light interception, which further verified the high accuracy of the light interception model. The annual average maize yield of 2M5S was 6.03% and 6.16% lower but the soybean yield was 23.69% and 28.52% higher than that of 2M3S. On the basis of system yield, the best performance was recorded in 2M4S at the two sites. In summary, the newly created light interception model performs well in the quantification of the temporal and spatial changes in crop light interception in strip intercropping and has potential applications in other configurations. Optimizing row configurations across climatic regions to enhance light interception and yield at the system level will become a future target.

Differential Effects of Sulfur Fertilization on Soil Microbial Communities and Maize Yield Enhancement

Sulfur (S) is an essential nutrient for plant growth, influencing not only crop yields but also the composition and function of soil microbial communities. However, the differential effects of S fertilization on abundant and rare taxa in agricultural soils remain poorly understood. This study investigates the impact of different S fertilizer types on maize yield and the structure and stability of soil microbial communities, with a particular focus on abundant and rare taxa. S fertilization led to significant increases maize yield on two typical soils (black soil and sandy soil) (5.3–24.3%) and altered soil properties, including reducing pH (0.04–0.20) and increasing the available sulfur (AS) content (3.8–8.0 mg kg−1), with ammonium sulfate having a more pronounced effect than elemental sulfur. Microbial analysis revealed distinct impacts on the diversity and community structure of both abundant and rare taxa. Elemental sulfur reduced the alpha diversity of abundant taxa more than ammonium sulfate, while NMDS indicated significant shifts in community structures, particularly among abundant taxa. Network analysis showed that S fertilization decreased the complexity of microbial interactions among rare taxa, with ammonium sulfate leading to simpler networks and elemental sulfur resulting in higher modularity. SEM highlighted that the diversity of rare taxa played a crucial role in influencing maize yield, alongside direct effects from soil properties such as AS and SAR (aryl sulfatase). Functional predictions demonstrated that amino acid metabolism and xenobiotic biodegradation and metabolism pathways were enriched in rare taxa, suggesting significant implications for soil health and crop productivity. This study provides new insights into the roles of abundant and rare bacterial taxa under S fertilization, emphasizing their importance in optimizing fertilization strategies for enhanced crop yield in specific soil types.

Rapid and Sensitive On-site Nucleic Acid Detection of Three Main Fusarium Pathogens of Maize Stalk Rot Based on RPA-CRISPR/Cas12a.

Maize stalk rot is a soil-borne disease that poses a serious threat to maize production worldwide, with the most significant cause being fungal stalk rot. The development of a visual and rapid detection method for the maize stalk rot pathogen is significant for its prompt and accurate identification, enhancing agricultural production efficiency, and implementing timely preventive measures. These measures will help safeguard the maize yield and quality, ultimately reducing agricultural losses. In this study, we aimed to develop an efficient method to detect maize stalk rot pathogens. We focused on three pathogenic fungi commonly found in maize-producing regions worldwide: Fusarium verticillioides, Fusarium proliferatum, and Fusarium graminearum. Based on TEF-1α, we developed a rapid detection technique using RPA-CRISPR/Cas12a, combined with test strips to develop an on-site rapid visual detection test for these pathogens. The method showed detection sensitivity for F. verticillioides, F. proliferatum, and F. graminearum within 20 min at concentrations of 7.8 pg/μL, 0.11 ng/μL, and 0.13 ng/μL, respectively. The sensitivity increased with increasing reaction time. Testing of field disease samples indicated that the method is effective in detecting nucleic acids obtained through crude extraction methods. In conclusion, we developed a visually rapid detection technology that does not rely on complex instruments and equipment for the on-site early detection of F. verticillioides, F. proliferatum, and F. graminearum in the field to implement effective control measures, ensuring stable and high maize yields.

Multivariate Analysis of Grain Yield and Main Agronomic Traits in Different Maize Hybrids Grown in Mountainous Areas

Inconsistent reports exist on the relationships between key agronomic traits and maize yield. We performed a multivariate analysis of yield and 10 agronomic traits in 59 hybrids to explore maize yields in mountainous areas. The yield per plant (YP) was significantly and positively correlated with kernel weight (KW), growth period (GP), and kernel row number (KRN). KW and KRN had positive effects on YP, whereas kernel rows per ear (KRE) had a negative effect. GP indirectly affected YP. GP, KW, KRN, and ear length (EL) showed the highest grey relational degree with YP. The first four principal components cumulatively accounted for 73.36% of variation. EL, KW, plant height (PH), ear height (EH), GP, KRN, and YP contributed positively to the variation, whereas KRE, shelling percentage (SP), bald-tip length (BTL), and ear girth (EG) contributed negatively. Based on trait similarity, the 59 maize hybrids were classified into two clusters, Clusters I and II. A total of 11 traits were grouped into four clusters, Clusters A–D. Cluster D included KW, GP, KRN, EL, EH, PH, and YP, and the 22 maize hybrids in Cluster I performed better in these traits. These results provide a theoretical basis for the breeding of high-yield maize varieties in mountainous areas.

Temperature increase may not necessarily penalize future yields of three major crops in Xinjiang, Northwest China

Future food production is at risk due to climate change, particularly in arid regions with limited water resources and extensive irrigated agriculture. This study utilized the DSSAT model in conjunction with downscaled data from 21 global climate models (GCMs) under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5) to assess the impact of projected climate change on irrigated crop phenology, yield, and evapotranspiration (ETc) of cotton, maize, and winter wheat in the Shihezi region of Xinjiang, China. The results indicated that temperature and precipitation in the region were expected to increase gradually from 2021 to 2100. Climate change has resulted in earlier anthesis and physiological maturity of all three crops. Future climatic conditions could reduce maize yields to 16.02 %. Conversely, the yields of cotton and winter wheat increased, with cotton yields rising by 1.23–10.94 % and winter wheat yields by 3.19–14.07 %. Additionally, ETc for cotton, maize, and winter wheat could rise in the future. The irrigation water demands could increase by 41.1–96.4 mm for cotton and 27.3–37.9 mm for maize, while the demand for winter wheat could decrease by 0.5–36.2 mm. Warming was significantly correlated with the changes in the yield and water use efficiency (WUE) of cotton, maize, and winter wheat. The temperature increases of +0.5°C to +3.0°C (relative to baseline) at 0.5°C intervals were analyzed to evaluate their effects on yield and WUE. The yields varied from −0.93 % to 6.15 % for cotton, −43.42 % to −7.99 % for maize, and 4.28–9.92 % for winter wheat. The WUE changes ranged from −29.03 % to −1.08 % for cotton, −43.06 % to −7.66 % for maize, and 0.69–3.47 % for winter wheat. Contrary to the common belief that rising temperatures generally harm crop yields, our study suggests that temperature fluctuations may benefit certain crops in specific regions. These results could provide theoretical guidance for implementing adaptive measures to future climate change in regions with conditions similar to Shihezi, Xinjiang, China, to ensure crop security and sustainable water management.

Residual herbicides applied to common bean cultivars on maize yield in succession

Given the introduction of increasingly productive and early maturing maize cultivars over the past decade, coupled with the restricted availability of herbicides for controlling eudicots weeds in common bean crops, leading producers have resorted to using mixtures of active ingredients. Consequently, there arises a necessity for studies to assess the carryover effects of these mixtures in succession crops. The objective of this study was to evaluate the residual effect of herbicides applied in common bean cultivars on maize, during two cultivation periods. The statistical design was a randomized complete block with four replications. The common bean cultivars BRS Pérola, IPR Tuiuiú, and BRSMG Marte were planted in the summer season 2016/2017 and in the winter season 2017, and subsequently, the maize DKB 390PRO was planted in the winter season and summer season, respectively. The experimental plots were treated with herbicides applied post-emergence, isolated or mixed, at the following rates [g a.i. ha-1]: fomesafen [250]; fomesafen [375]; bentazon + imazamox [600+28]; bentazon + imazamox + fomesafen [(600+28)+125]; and bentazon + imazamox + fomesafen [(600+28)+87.5]; and hand weeding without herbicide. The herbicides did not cause negative effects on maize yield, regardless of the sowing period and common bean cultivar.

Prediction of Maize Crop Yield Using Principal Component Analysis of Weather Parameters

The use of principal component analysis in the development of statistical models for crop yield forecasting has been demonstrated. Maize crop yield data for a period of 21 years (2001-2021) were drawn from the Dacnet website and the weather data were collected from the Meteorological Observatory, Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology Pantnagar, Uttarakhand. Maximum temperature, Minimum temperature, Relative Humidity A.M, Relative Humidity P.M, Total rainfall, Sunshine hours, Wind velocity and Evapotranspiration were the weather parameters considered for the study. Out of the 21-year data, 17-year data were used for training the model while remaining 4 years data were used for testing the model. Weekly data on weather variables was used to create weather indices [1]. This work involves developing forecasting models using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Applied to extract principal components (PCs) from the correlation matrix of predictors. Five models (Model 1 to Model 5) are developed using MLR, with varying numbers of PCs as regressors which also include time trend and maize yield as dependent variable. The model performance was measured using Adjusted R-squared (adj R2) and Root Mean Square Error (RMSE) as goodness of fit criteria. On the basis of adj R2 and RMSE, model 1 which includes all the calculated weather indices, was found to be best suited model with high adj R2 (74.18 %) and least RMSE (276.36). Hence, this model can be used to forecast maize yield for the studied region.

Leaf-whorl inoculation with Sporisorium reilianum may overcome field resistance of maize.

Maize yield is threatened by increasing incidences of head smut disease caused by Sporisorium reilianum. To help breeders identify S. reilianum-resistant maize lines, the availability of efficient screening systems would be an advantage. Here we assessed maize lines with distinct levels of field resistance against head smut disease in greenhouse experiments using two different inoculation techniques. Addition of mixtures of mating-compatible sporidia to the soil at seedling stage of the plant did not lead to plant disease, and we could detect only marginal amounts of fungal DNA in apical meristems at eighteen days after sowing. Inoculation of the maize lines by leaf-whorl inoculation led to both high disease incidence and prominent levels of fungal DNA in apical meristems in all tested maize lines regardless of their field resistance levels. Thus, S. reilianum entering the plant via the leaf whorl can escape existing resistance mechanisms of currently known field-resistant maize lines. Since field-resistant lines are also resistant to inoculation via teliospore-contaminated soil, we propose teliospore addition to seeds at the time of sowing (rather than leaf-whorl inoculation of seedlings) combined with quantitative detection of fungal DNA in apical meristems, as an efficient screening procedure to discover field-resistant lines. However, screening maize plants for resistance against the leaf-whorl inoculation method might be promising for the discovery of novel resistance mechanisms needed to develop durably resistant maize lines.

APPLICATION OF ORGANIC AND INORGANIC NUTRIENT SOURCES FOR IMPROVING GROWTH AND YIELD ATTRIBUTES OF MAIZE (ZEA MAYS L.) UNDER CLIMATIC CONDITIONS OF PAKISTAN

The research was conducted under rain fed conditions of Rawalakot to check the effect of different organic and chemical fertilizers on yield and yield components in maize. Eight different treatments were evaluated by using regular arrangement of randomized complete block design (RCBD) with three replications. Sheep manure (SM), farm yard manure (FYM), and poultry manure (PM) were used as organic sources of nutrients while NPK was used inorganic sources. The test variety of maize was Pioneer-30Y87. The data was subjected to analysis of variance (ANOVA) to analyze the difference amongst different means using least significance difference (LSD). Results revealed significant effect of different nutrient treatments on the yield and morphological growth of maize. Maximum number of plants (11.00), plant height (212.80 cm), number of grain rows per cob (15.60), 1000 grains weight (188.90 g), grain yield (6836.7g) were recorded for Treatment T7 (Half recommended NPK fertilizer + farm yard manure. tons ha-1). This study showed that mixed use of inorganic and organic sources of nutrients could be a better soil nutrient amendment when applied at a ratio of 50% dose of farm yard manure (FYM) + 50% dose of NPK in the cultivation of maize under rain fed conditions of Rawalakot.

Potential genetic gains from targeted recombination versus recurrent selection for maize yield and moisture

AbstractRecurrent selection is a proven method and targeted recombination is a potential method for achieving genetic gain. Our objectives were to determine the (1) equivalency in predicted gains between cycles of recurrent selection and targeted recombination in maize (Zea mays L.), (2) correspondence between targeted‐recombination positions when gains from nontargeted chromosomes are considered versus ignored, and (3) trade‐offs in predicted gains for yield and moisture in targeted recombination. From genomewide marker effects for 2911 single nucleotide polymorphism loci in 270 biparental populations, we obtained predicted gains from fixed numbers of targeted recombinations and compared these gains to the genotypic‐value distributions of doubled haploids obtained from different cycles of simulated recurrent selection. On average, it took three cycles of single‐trait recurrent selection to obtain at least a few doubled haploid lines with the same performance achieved with nine or 10 targeted recombinations in Cycle 0. Results indicated that when targeting recombinations across the genome, gains from segregation on nontargeted chromosomes do not need to be considered. Predicted increases in yield were associated with increases in moisture. Given the logistics of pyramiding targeted recombinations and the lack of mature technologies for routine targeted recombination, recurrent selection is currently the preferred approach for accumulating favorable recombinations.

Optimizing planting density enhances the multi-scale structural characteristics and in vitro digestibility of maize starch via modulating the size distribution of granules

The modification of starch through agricultural practices is becoming increasingly significant for producing healthy foodstuffs and raw materials for industrial applications, consequently gaining momentum in academic research. This study examined how three different planting densities influenced the distribution of granule sizes, multi-scale structural characteristics, and in vitro digestibility of maize starch. The results showed that planting density significantly enhanced grain yield and relative crystallinity, and significant increases were also observed in the contents of both rapidly and slowly digestible starch. The surface- and volume-weighted mean diameter of granules significantly increased under the medium level (6.75 × 104 plants ha−1), and then decreased under high planting density level. As planting density level increased, the amylose content, peak viscosity, and hardness varied from 23.3 to 26.4 %, from 1962 to 2659 mPa·s, and from 129.3 to 307.6 g, respectively. However, no change was found in crystalline structure of maize starch. These results indicated that optimizing planting density could effectively improve grain yield and starch characteristics of maize, with the best effect under the level of 6.75 × 104 plants ha−1.

In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations

Various yield forecasting methods have been reported in literature, but the benefits of assimilating seasonal climate forecasts and satellite observations for in-season yield forecasting during different growth stages have rarely been examined using machine learning. By synthesizing census yields, seasonal climate forecasts (SCF) and satellite-based gross primary production (GPP), this study develops a machine learning (i.e., Random Forest)–based in-season maize yield forecasting model for Northeast China, which produces over 40 % of China's total maize production. Based on the dynamically trained model, 11 numerical experiments are conducted to investigate the benefits of assimilating SCF and GPP relative to the experiments without using SCF and GPP for yield prediction in four forecast phases (planting–three leaves, three leaves–jointing, jointing–tasseling, tasseling–milk). Generally, our yield forecasting model exhibits a promising skill with a low bias of 4.11 %−4.97 %, when the observed climate and GPP are used as inputs. When climate forecasts are assimilated into the model by sampling historical climate, satisfactory yield forecasting can be achieved one month before harvest with a bias of 5.50 %−5.63 %. Bias-correcting climate forecast data from dynamical weather forecast models has a larger benefit for yield prediction with a lower bias of 4.77 %−5.06 %. Furthermore, we found a better benefit of assimilating SCF when compared with GPP during the first three forecast phases, although its relative importance decreases substantially towards harvest. Finally, phase-dependent maps indicating the best model are produced for each county, with historical resampling methods performing best in 40 %, 32 %, 27 %, and 21 % of counties from Phase 1 to Phase 4 and dynamical weather forecast models showing greater predictability in 54 %, 58 %, 58 %, and 52 % of counties during Phases 1–4, respectively. This study provides a useful yield forecasting framework for the breadbasket of China, which can be extended to other crops.